Two-Source Validation of Online Surface EMG Decomposition Using Progressive FastICA Peel-off.

Abstract

Recently, great interests have been attracted on the online decomposition of surface electromyogram (SEMG) but current studies mainly performed validation on simulated EMG signals due to the fact that real MU activities in experimental signals were unknown. For a more comprehensive assessment of online SEMG decomposition, a two-source validation was conducted by simultaneously collecting intramuscular EMG (IEMG) and high-density SEMG signals. The IEMG signal was decomposed using a simplified version of Progressive FastICA Peel-off (PFP) method with a combination of the peel-off strategy and the valley-seeking clustering, and the decomposed motor unit (MU) spike trains were used as the ground-truth reference. For SEMG recordings, the signals within initial 5 seconds were used to offline obtain MU separation vectors and these vectors were subsequently employed to extract MU spike trains in the online stage. The matching rate of the common firing events from the ground-truth reference and online SEMG decomposition were calculated and assessed. A total of 549 and 92 MUs were identified from the SEMG and IEMG signals from 5 healthy subjects' first dorsal interosseous muscle. All the MUs decomposed from IEMG can be matched with MUs from online SEMG decomposition and the average matching rate in the online stage was (96 ± 1) %. The results highlighted the ability of separation vectors to continuously and precisely track the same MU in the experimental SEMG signals. Our study provides a more comprehensive validation perspective of online SEMG decomposition on the experimental data.